Abstract
In this paper, aiming at the characteristics of Chinese text classification, using the ICTCLAS(Chinese lexical analysis system of Chinese academy of sciences) for document segmentation, and for data cleaning and filtering the Stop words, using the information gain and document frequency feature selection algorithm to document feature selection. Based on this, based on the Naive Bayesian algorithm implemented text classifier , and use Chinese corpus of Fudan University has carried on the experiment and analysis on the system.
Highlights
With large network retrieval system, document management, information filtering system, such as wide applicationthe growing importance of text categorization have been increasingly emerging DŽ The common method to text classification are mainly Support Vector Machine method, the K-Nearest Neighbor, Naive Bayes theorem, Neural Net, etc
This article is based on Naive Bayes algorithm and Chinese lexical analysis system ICTCLAS, design a suitable Chinese text categorization system
The word and the word was separated by Spaces, don't need word processing
Summary
With large network retrieval system, document management, information filtering system, such as wide applicationthe growing importance of text categorization have been increasingly emerging DŽ The common method to text classification are mainly Support Vector Machine method, the K-Nearest Neighbor , Naive Bayes theorem, Neural Net, etc. Take out of stop words can largely reduce the number of feature item, has great help for text dimension reduction, so in front of the vector space model , to clean up the words thoroughly which are without help for classify.
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